




import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

	if is_vision_available():
		from transformers import MaskaFormerImageProcessor

if is_vision_available():
	from PIL import Image


class    SCREAMING_SNAKE_CASE_				:



	"""simple docstring"""


	def __init__(					self		:						Optional[Any]					,     lowerCAmelCase		:						Union[str, Any]					,     lowerCAmelCase		:						int=2					,     lowerCAmelCase		:						Dict=True					,     lowerCAmelCase		:						str=False					,     lowerCAmelCase		:						Any=10					,     lowerCAmelCase		:						Dict=3					,     lowerCAmelCase		:						Tuple=32 * 8					,     lowerCAmelCase		:						Tuple=32 * 8					,     lowerCAmelCase		:						Dict=4					,     lowerCAmelCase		:						Optional[int]=64					,     )	->       int:







						"""simple docstring"""







						__UpperCamelCase					:	Union[str, Any]             =      parent
						__UpperCamelCase					:	Union[str, Any]             =      batch_size
						__UpperCamelCase					:	Union[str, Any]             =      is_training
						__UpperCamelCase					:	Tuple             =      use_auxiliary_loss
						__UpperCamelCase					:	Optional[int]             =      num_queries
						__UpperCamelCase					:	str             =      num_channels
						__UpperCamelCase					:	List[str]             =      min_size
						__UpperCamelCase					:	Dict             =      max_size
						__UpperCamelCase					:	List[str]             =      num_labels
						__UpperCamelCase					:	Tuple             =      hidden_dim
						__UpperCamelCase					:	int             =      hidden_dim






	def 					lowerCamelCase__     (					self		:						Tuple	)	->       Optional[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	Tuple             =      floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]	).to(
						    lowerCAmelCase	)

						__UpperCamelCase					:	List[Any]             =      torch.ones([self.batch_size, self.min_size, self.max_size]					,     device=lowerCAmelCase	)

						__UpperCamelCase					:	Optional[int]             =      (
						    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]					,     device=lowerCAmelCase	) > 0.5
						).float()
						__UpperCamelCase					:	Dict             =      (torch.rand((self.batch_size, self.num_labels)					,     device=lowerCAmelCase	) > 0.5).long()

						__UpperCamelCase					:	Any             =      self.get_config()
						return config, pixel_values, pixel_mask, mask_labels, class_labels






	def 					lowerCamelCase__     (					self		:						List[str]	)	->       Dict:







						"""simple docstring"""







						__UpperCamelCase					:	List[str]             =      MaskaFormerConfig(
						    hidden_size=self.hidden_dim					,     )
						__UpperCamelCase					:	Tuple             =      self.num_queries
						__UpperCamelCase					:	str             =      self.num_labels

						__UpperCamelCase					:	Tuple             =      [1, 1, 1, 1]
						__UpperCamelCase					:	Union[str, Any]             =      self.num_channels

						__UpperCamelCase					:	Optional[int]             =      64
						__UpperCamelCase					:	Optional[Any]             =      128
						__UpperCamelCase					:	Any             =      self.hidden_dim
						__UpperCamelCase					:	str             =      self.hidden_dim
						__UpperCamelCase					:	Any             =      self.hidden_dim
						return config






	def 					lowerCamelCase__     (					self		:						List[str]	)	->       Union[str, Any]:







						"""simple docstring"""







						__UpperCamelCase					:	Tuple             =      self.prepare_config_and_inputs()
						__UpperCamelCase					:	Any             =      {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
						return config, inputs_dict






	def 					lowerCamelCase__     (					self		:						Tuple					,     lowerCAmelCase		:						Tuple					,     lowerCAmelCase		:						Any	)	->       Optional[int]:







						"""simple docstring"""







						__UpperCamelCase					:	List[str]             =      output.encoder_hidden_states
						__UpperCamelCase					:	List[str]             =      output.pixel_decoder_hidden_states
						__UpperCamelCase					:	Optional[int]             =      output.transformer_decoder_hidden_states

						self.parent.assertTrue(len(lowerCAmelCase	)					,     len(config.backbone_config.depths	)	)
						self.parent.assertTrue(len(lowerCAmelCase	)					,     len(config.backbone_config.depths	)	)
						self.parent.assertTrue(len(lowerCAmelCase	)					,     config.decoder_layers	)






	def 					lowerCamelCase__     (					self		:						Dict					,     lowerCAmelCase		:						List[Any]					,     lowerCAmelCase		:						List[Any]					,     lowerCAmelCase		:						List[str]					,     lowerCAmelCase		:						Union[str, Any]=False	)	->       Any:







						"""simple docstring"""







						with torch.no_grad():
											__UpperCamelCase					:	List[Any]             =      MaskaFormerModel(config=lowerCAmelCase	)
											model.to(lowerCAmelCase	)
											model.eval()

											__UpperCamelCase					:	Optional[int]             =      model(pixel_values=lowerCAmelCase					,     pixel_mask=lowerCAmelCase	)
											__UpperCamelCase					:	Tuple             =      model(lowerCAmelCase					,     output_hidden_states=lowerCAmelCase	)

						self.parent.assertEqual(
						    output.transformer_decoder_last_hidden_state.shape					,     (self.batch_size, self.num_queries, self.hidden_dim)					,     )
						# let's ensure the other two hidden state exists
						self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None	)
						self.parent.assertTrue(output.encoder_last_hidden_state is not None	)

						if output_hidden_states:
											self.check_output_hidden_state(lowerCAmelCase					,     lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Optional[Any]					,     lowerCAmelCase		:						List[Any]					,     lowerCAmelCase		:						Union[str, Any]					,     lowerCAmelCase		:						int					,     lowerCAmelCase		:						Dict					,     lowerCAmelCase		:						str	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	Any             =      MaskaFormerForUniversalSegmentation(config=lowerCAmelCase	)
						model.to(lowerCAmelCase	)
						model.eval()

						def comm_check_on_output(lowerCAmelCase		:						Any	):
											# let's still check that all the required stuff is there
											self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None	)
											self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None	)
											self.parent.assertTrue(result.encoder_last_hidden_state is not None	)
											# okay, now we need to check the logits shape
											# due to the encoder compression, masks have a //4 spatial size
											self.parent.assertEqual(
											    result.masks_queries_logits.shape					,     (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)					,     )
											# + 1 for null class
											self.parent.assertEqual(
											    result.class_queries_logits.shape					,     (self.batch_size, self.num_queries, self.num_labels + 1)	)

						with torch.no_grad():
											__UpperCamelCase					:	Optional[int]             =      model(pixel_values=lowerCAmelCase					,     pixel_mask=lowerCAmelCase	)
											__UpperCamelCase					:	str             =      model(lowerCAmelCase	)

											comm_check_on_output(lowerCAmelCase	)

											__UpperCamelCase					:	Optional[int]             =      model(
											    pixel_values=lowerCAmelCase					,     pixel_mask=lowerCAmelCase					,     mask_labels=lowerCAmelCase					,     class_labels=lowerCAmelCase	)

						comm_check_on_output(lowerCAmelCase	)

						self.parent.assertTrue(result.loss is not None	)
						self.parent.assertEqual(result.loss.shape					,     torch.Size([1]	)	)






@require_torch
class    SCREAMING_SNAKE_CASE_				(       __lowercase	,  __lowercase	,  unittest.TestCase   ):



	"""simple docstring"""


	__magic_name__   :       str      	=		(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
	__magic_name__   :       Optional[int]      	=		{'feature-extraction': MaskaFormerModel} if is_torch_available() else {}

	__magic_name__   :       int      	=		False
	__magic_name__   :       Dict      	=		False
	__magic_name__   :       Optional[Any]      	=		False
	__magic_name__   :       List[Any]      	=		False






	def 					lowerCamelCase__     (					self		:						Optional[int]	)	->       Optional[int]:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      MaskaFormerModelTester(self	)
						__UpperCamelCase					:	int             =      ConfigTester(self					,     config_class=lowerCAmelCase					,     has_text_modality=lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						str	)	->       List[Any]:







						"""simple docstring"""







						self.config_tester.run_common_tests()






	def 					lowerCamelCase__     (					self		:						Tuple	)	->       Any:







						"""simple docstring"""







						__UpperCamelCase					:	Union[str, Any]             =      self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase					,     **lowerCAmelCase					,     output_hidden_states=lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Any	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[Any]             =      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCAmelCase	)






	@unittest.skip(reason="""Mask2Former does not use inputs_embeds"""	)
	def 					lowerCamelCase__     (					self		:						Dict	)	->       Optional[int]:







						"""simple docstring"""







						pass






	@unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method"""	)
	def 					lowerCamelCase__     (					self		:						Optional[Any]	)	->       Any:







						"""simple docstring"""







						pass






	@unittest.skip(reason="""Mask2Former is not a generative model"""	)
	def 					lowerCamelCase__     (					self		:						Any	)	->       List[str]:







						"""simple docstring"""







						pass






	@unittest.skip(reason="""Mask2Former does not use token embeddings"""	)
	def 					lowerCamelCase__     (					self		:						Any	)	->       Tuple:







						"""simple docstring"""







						pass






	@require_torch_multi_gpu
	@unittest.skip(
	    reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""	)
	def 					lowerCamelCase__     (					self		:						Tuple	)	->       Union[str, Any]:







						"""simple docstring"""







						pass






	@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""	)
	def 					lowerCamelCase__     (					self		:						Dict	)	->       List[str]:







						"""simple docstring"""







						pass






	def 					lowerCamelCase__     (					self		:						Union[str, Any]	)	->       int:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											__UpperCamelCase					:	Dict             =      model_class(lowerCAmelCase	)
											__UpperCamelCase					:	Any             =      inspect.signature(model.forward	)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											__UpperCamelCase					:	Dict             =      [*signature.parameters.keys()]

											__UpperCamelCase					:	int             =      ["pixel_values"]
											self.assertListEqual(arg_names[:1]					,     lowerCAmelCase	)






	@slow
	def 					lowerCamelCase__     (					self		:						int	)	->       Optional[Any]:







						"""simple docstring"""







						for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
											__UpperCamelCase					:	Optional[Any]             =      MaskaFormerModel.from_pretrained(lowerCAmelCase	)
											self.assertIsNotNone(lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						str	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[int]             =      (self.model_tester.min_size,) * 2
						__UpperCamelCase					:	List[Any]             =      {
						    "pixel_values": torch.randn((2, 3, *size)					,     device=lowerCAmelCase	),
						    "mask_labels": torch.randn((2, 10, *size)					,     device=lowerCAmelCase	),
						    "class_labels": torch.zeros(2					,     10					,     device=lowerCAmelCase	).long(),
						}
						__UpperCamelCase					:	List[str]             =      self.model_tester.get_config()

						__UpperCamelCase					:	Any             =      MaskaFormerForUniversalSegmentation(lowerCAmelCase	).to(lowerCAmelCase	)
						__UpperCamelCase					:	int             =      model(**lowerCAmelCase	)
						self.assertTrue(outputs.loss is not None	)






	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       str:







						"""simple docstring"""







						__UpperCamelCase					:	List[Any]             =      self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase					,     **lowerCAmelCase					,     output_hidden_states=lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						int	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[Any]             =      self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											__UpperCamelCase					:	List[Any]             =      model_class(lowerCAmelCase	).to(lowerCAmelCase	)
											__UpperCamelCase					:	str             =      model(**lowerCAmelCase					,     output_attentions=lowerCAmelCase	)
											self.assertTrue(outputs.attentions is not None	)






	def 					lowerCamelCase__     (					self		:						str	)	->       List[str]:







						"""simple docstring"""







						if not self.model_tester.is_training:
											return

						__UpperCamelCase					:	List[str]             =      self.all_model_classes[1]
						__UpperCamelCase					:	Optional[int]             =      self.model_tester.prepare_config_and_inputs()

						__UpperCamelCase					:	int             =      model_class(lowerCAmelCase	)
						model.to(lowerCAmelCase	)
						model.train()

						__UpperCamelCase					:	Optional[int]             =      model(lowerCAmelCase					,     mask_labels=lowerCAmelCase					,     class_labels=lowerCAmelCase	).loss
						loss.backward()






	def 					lowerCamelCase__     (					self		:						Any	)	->       Optional[int]:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[int]             =      self.all_model_classes[1]
						__UpperCamelCase					:	Any             =      self.model_tester.prepare_config_and_inputs()
						__UpperCamelCase					:	Any             =      True
						__UpperCamelCase					:	List[Any]             =      True

						__UpperCamelCase					:	str             =      model_class(lowerCAmelCase	).to(lowerCAmelCase	)
						model.train()

						__UpperCamelCase					:	Optional[int]             =      model(lowerCAmelCase					,     mask_labels=lowerCAmelCase					,     class_labels=lowerCAmelCase	)

						__UpperCamelCase					:	Optional[Any]             =      outputs.encoder_hidden_states[0]
						encoder_hidden_states.retain_grad()

						__UpperCamelCase					:	Union[str, Any]             =      outputs.pixel_decoder_hidden_states[0]
						pixel_decoder_hidden_states.retain_grad()

						__UpperCamelCase					:	Tuple             =      outputs.transformer_decoder_hidden_states[0]
						transformer_decoder_hidden_states.retain_grad()

						__UpperCamelCase					:	List[str]             =      outputs.attentions[0]
						attentions.retain_grad()

						outputs.loss.backward(retain_graph=lowerCAmelCase	)

						self.assertIsNotNone(encoder_hidden_states.grad	)
						self.assertIsNotNone(pixel_decoder_hidden_states.grad	)
						self.assertIsNotNone(transformer_decoder_hidden_states.grad	)
						self.assertIsNotNone(attentions.grad	)






a__		     =     1e-4





def 				A__ ()     ->						Optional[Any]:
					__UpperCamelCase					:	Optional[Any]             =      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
					return image







@require_vision
@slow
class    SCREAMING_SNAKE_CASE_				(       unittest.TestCase   ):



	"""simple docstring"""


	@cached_property
	def 					lowerCamelCase__     (					self		:						Optional[Any]	)	->       Any:







						"""simple docstring"""







						return "facebook/mask2former-swin-small-coco-instance"






	@cached_property
	def 					lowerCamelCase__     (					self		:						int	)	->       int:







						"""simple docstring"""







						return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints	) if is_vision_available() else None






	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[Any]             =      MaskaFormerModel.from_pretrained(self.model_checkpoints	).to(lowerCAmelCase	)
						__UpperCamelCase					:	Optional[int]             =      self.default_image_processor
						__UpperCamelCase					:	List[Any]             =      prepare_img()
						__UpperCamelCase					:	Any             =      image_processor(lowerCAmelCase					,     return_tensors="""pt"""	).to(lowerCAmelCase	)
						__UpperCamelCase					:	Any             =      inputs["pixel_values"].shape
						# check size is divisible by 32
						self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0	)
						# check size
						self.assertEqual(lowerCAmelCase					,     (1, 3, 384, 384)	)

						with torch.no_grad():
											__UpperCamelCase					:	List[str]             =      model(**lowerCAmelCase	)

						__UpperCamelCase					:	str             =      torch.tensor(
						    [[-0.27_90, -1.07_17, -1.16_68], [-0.51_28, -0.31_28, -0.49_87], [-0.58_32, 0.19_71, -0.01_97]]	).to(lowerCAmelCase	)
						self.assertTrue(
						    torch.allclose(
						        outputs.encoder_last_hidden_state[0, 0, :3, :3]					,     lowerCAmelCase					,     atol=lowerCAmelCase	)	)

						__UpperCamelCase					:	Any             =      torch.tensor(
						    [[0.89_73, 1.18_47, 1.17_76], [1.19_34, 1.50_40, 1.51_28], [1.11_53, 1.44_86, 1.49_51]]	).to(lowerCAmelCase	)
						self.assertTrue(
						    torch.allclose(
						        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]					,     lowerCAmelCase					,     atol=lowerCAmelCase	)	)

						__UpperCamelCase					:	Dict             =      torch.tensor(
						    [[2.11_52, 1.70_00, -0.86_03], [1.58_08, 1.80_04, -0.93_53], [1.60_43, 1.74_95, -0.59_99]]	).to(lowerCAmelCase	)
						self.assertTrue(
						    torch.allclose(
						        outputs.transformer_decoder_last_hidden_state[0, :3, :3]					,     lowerCAmelCase					,     atol=lowerCAmelCase	)	)






	def 					lowerCamelCase__     (					self		:						Tuple	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Union[str, Any]             =      MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints	).to(lowerCAmelCase	).eval()
						__UpperCamelCase					:	str             =      self.default_image_processor
						__UpperCamelCase					:	str             =      prepare_img()
						__UpperCamelCase					:	Optional[Any]             =      image_processor(lowerCAmelCase					,     return_tensors="""pt"""	).to(lowerCAmelCase	)
						__UpperCamelCase					:	str             =      inputs["pixel_values"].shape
						# check size is divisible by 32
						self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0	)
						# check size
						self.assertEqual(lowerCAmelCase					,     (1, 3, 384, 384)	)

						with torch.no_grad():
											__UpperCamelCase					:	int             =      model(**lowerCAmelCase	)
						# masks_queries_logits
						__UpperCamelCase					:	Optional[Any]             =      outputs.masks_queries_logits
						self.assertEqual(
						    masks_queries_logits.shape					,     (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)	)
						__UpperCamelCase					:	str             =      [
						    [-8.78_39, -9.00_56, -8.81_21],
						    [-7.41_04, -7.03_13, -6.54_01],
						    [-6.61_05, -6.34_27, -6.46_75],
						]
						__UpperCamelCase					:	Tuple             =      torch.tensor(lowerCAmelCase	).to(lowerCAmelCase	)
						self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]					,     lowerCAmelCase					,     atol=lowerCAmelCase	)	)
						# class_queries_logits
						__UpperCamelCase					:	Optional[int]             =      outputs.class_queries_logits
						self.assertEqual(class_queries_logits.shape					,     (1, model.config.num_queries, model.config.num_labels + 1)	)
						__UpperCamelCase					:	str             =      torch.tensor(
						    [
						        [1.83_24, -8.08_35, -4.19_22],
						        [0.84_50, -9.00_50, -3.60_53],
						        [0.30_45, -7.72_93, -3.02_75],
						    ]	).to(lowerCAmelCase	)
						self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]					,     lowerCAmelCase					,     atol=lowerCAmelCase	)	)






	def 					lowerCamelCase__     (					self		:						int	)	->       Any:







						"""simple docstring"""







						__UpperCamelCase					:	Union[str, Any]             =      MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints	).to(lowerCAmelCase	).eval()
						__UpperCamelCase					:	Dict             =      self.default_image_processor

						__UpperCamelCase					:	Tuple             =      image_processor(
						    [np.zeros((3, 800, 1333)	), np.zeros((3, 800, 1333)	)]					,     segmentation_maps=[np.zeros((384, 384)	).astype(np.floataa	), np.zeros((384, 384)	).astype(np.floataa	)]					,     return_tensors="""pt"""					,     )

						__UpperCamelCase					:	Any             =      inputs["pixel_values"].to(lowerCAmelCase	)
						__UpperCamelCase					:	Any             =      [el.to(lowerCAmelCase	) for el in inputs["mask_labels"]]
						__UpperCamelCase					:	Union[str, Any]             =      [el.to(lowerCAmelCase	) for el in inputs["class_labels"]]

						with torch.no_grad():
											__UpperCamelCase					:	Optional[int]             =      model(**lowerCAmelCase	)

						self.assertTrue(outputs.loss is not None	)








import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase    :   List[str]						=			get_tests_dir('fixtures/test_sentencepiece.model')




@require_sentencepiece
@require_tokenizers
class 					lowerCamelCase__				(							__lowercase	,		unittest.TestCase):




	'''simple docstring'''




	_A								=	ReformerTokenizer
	_A								=	ReformerTokenizerFast
	_A								=	True
	_A								=	False
	_A								=	True







	def   _lowerCamelCase							(   self						:Dict		)						->					Union[str, Any]:
								super().setUp()

								__UpperCamelCase      :  List[str]       =   ReformerTokenizer(a	,  keep_accents=a		)
								tokenizer.save_pretrained(self.tmpdirname		)







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Dict:
								__UpperCamelCase      :  int       =   "<s>"
								__UpperCamelCase      :  Dict       =   1

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(a		)	,  a		)
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(a		)	,  a		)







	def   _lowerCamelCase							(   self						:Dict		)						->					List[Any]:
								__UpperCamelCase      :  int       =   list(self.get_tokenizer().get_vocab().keys()		)

								self.assertEqual(vocab_keys[0]	,  "<unk>"		)
								self.assertEqual(vocab_keys[1]	,  "<s>"		)
								self.assertEqual(vocab_keys[-1]	,  "j"		)
								self.assertEqual(len(a		)	,  1_0_0_0		)







	def   _lowerCamelCase							(   self						:List[str]		)						->					str:
								self.assertEqual(self.get_tokenizer().vocab_size	,  1_0_0_0		)







	def   _lowerCamelCase							(   self						:List[str]		)						->					List[str]:
								if not self.test_rust_tokenizer:
															return

								__UpperCamelCase      :  Union[str, Any]       =   self.get_tokenizer()
								__UpperCamelCase      :  List[str]       =   self.get_rust_tokenizer()

								__UpperCamelCase      :  Union[str, Any]       =   "I was born in 92000, and this is falsé."

								__UpperCamelCase      :  List[str]       =   tokenizer.tokenize(a		)
								__UpperCamelCase      :  int       =   rust_tokenizer.tokenize(a		)
								self.assertListEqual(a	,  a		)

								__UpperCamelCase      :  List[str]       =   tokenizer.encode(a	,  add_special_tokens=a		)
								__UpperCamelCase      :  Tuple       =   rust_tokenizer.encode(a	,  add_special_tokens=a		)
								self.assertListEqual(a	,  a		)

								__UpperCamelCase      :  Union[str, Any]       =   self.get_rust_tokenizer()
								__UpperCamelCase      :  int       =   tokenizer.encode(a		)
								__UpperCamelCase      :  List[Any]       =   rust_tokenizer.encode(a		)
								self.assertListEqual(a	,  a		)







	def   _lowerCamelCase							(   self						:Tuple	,  a						:int=1_5		)						->					Any:
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'		):
																						__UpperCamelCase      :  List[Any]       =   self.rust_tokenizer_class.from_pretrained(a	,  **a		)

																						# Simple input
																						__UpperCamelCase      :  Union[str, Any]       =   "This is a simple input"
																						__UpperCamelCase      :  Dict       =   ["This is a simple input 1", "This is a simple input 2"]
																						__UpperCamelCase      :  Tuple       =   ("This is a simple input", "This is a pair")
																						__UpperCamelCase      :  Optional[Any]       =   [
																						    ("This is a simple input 1", "This is a simple input 2"),
																						    ("This is a simple pair 1", "This is a simple pair 2"),
																						]

																						# Simple input tests
																						self.assertRaises(a	,  tokenizer_r.encode	,  a	,  max_length=a	,  padding="max_length"		)

																						# Simple input
																						self.assertRaises(a	,  tokenizer_r.encode_plus	,  a	,  max_length=a	,  padding="max_length"		)

																						# Simple input
																						self.assertRaises(
																						    a	,  tokenizer_r.batch_encode_plus	,  a	,  max_length=a	,  padding="max_length"	,  )

																						# Pair input
																						self.assertRaises(a	,  tokenizer_r.encode	,  a	,  max_length=a	,  padding="max_length"		)

																						# Pair input
																						self.assertRaises(a	,  tokenizer_r.encode_plus	,  a	,  max_length=a	,  padding="max_length"		)

																						# Pair input
																						self.assertRaises(
																						    a	,  tokenizer_r.batch_encode_plus	,  a	,  max_length=a	,  padding="max_length"	,  )







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					str:
								pass







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Optional[Any]:
								__UpperCamelCase      :  List[Any]       =   ReformerTokenizer(a	,  keep_accents=a		)

								__UpperCamelCase      :  Tuple       =   tokenizer.tokenize("This is a test"		)
								self.assertListEqual(a	,  ["▁This", "▁is", "▁a", "▁t", "est"]		)

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(a		)	,  [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]	,  )

								__UpperCamelCase      :  str       =   tokenizer.tokenize("I was born in 92000, and this is falsé."		)
								self.assertListEqual(
								    a	,  [
								        SPIECE_UNDERLINE + "I",
								        SPIECE_UNDERLINE + "was",
								        SPIECE_UNDERLINE + "b",
								        "or",
								        "n",
								        SPIECE_UNDERLINE + "in",
								        SPIECE_UNDERLINE + "",
								        "9",
								        "2",
								        "0",
								        "0",
								        "0",
								        ",",
								        SPIECE_UNDERLINE + "and",
								        SPIECE_UNDERLINE + "this",
								        SPIECE_UNDERLINE + "is",
								        SPIECE_UNDERLINE + "f",
								        "al",
								        "s",
								        "é",
								        ".",
								    ]	,  )
								__UpperCamelCase      :  Union[str, Any]       =   tokenizer.convert_tokens_to_ids(a		)
								self.assertListEqual(
								    a	,  [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]	,  )

								__UpperCamelCase      :  Union[str, Any]       =   tokenizer.convert_ids_to_tokens(a		)
								self.assertListEqual(
								    a	,  [
								        SPIECE_UNDERLINE + "I",
								        SPIECE_UNDERLINE + "was",
								        SPIECE_UNDERLINE + "b",
								        "or",
								        "n",
								        SPIECE_UNDERLINE + "in",
								        SPIECE_UNDERLINE + "",
								        "<unk>",
								        "2",
								        "0",
								        "0",
								        "0",
								        ",",
								        SPIECE_UNDERLINE + "and",
								        SPIECE_UNDERLINE + "this",
								        SPIECE_UNDERLINE + "is",
								        SPIECE_UNDERLINE + "f",
								        "al",
								        "s",
								        "<unk>",
								        ".",
								    ]	,  )







	@cached_property
	def   _lowerCamelCase							(   self						:Any		)						->					List[str]:
								return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment"		)







	@slow
	def   _lowerCamelCase							(   self						:List[Any]		)						->					Dict:
								__UpperCamelCase      :  int       =   "Hello World!"
								__UpperCamelCase      :  Union[str, Any]       =   [1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]

								self.assertListEqual(a	,  self.big_tokenizer.encode(a		)		)







	@slow
	def   _lowerCamelCase							(   self						:Optional[Any]		)						->					List[str]:
								__UpperCamelCase      :  Dict       =   (
								    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
								    " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
								)
								__UpperCamelCase      :  List[Any]       =   [
								    1_0_8,
								    2_6_5,
								    2_4,
								    1_1_1,
								    4,
								    2_5_8,
								    1_5_6,
								    3_5,
								    2_8,
								    2_7_5,
								    3,
								    2_5_9,
								    2_9_7,
								    2_6_0,
								    8_4,
								    4,
								    3_5,
								    1_1_0,
								    4_4,
								    8,
								    2_5_9,
								    9_1,
								    2_6_8,
								    2_1,
								    1_1,
								    2_0_9,
								    2_7_4,
								    1_0_9,
								    2_6_6,
								    2_7_7,
								    1_1_7,
								    8_6,
								    9_3,
								    3_1_5,
								    2_5_8,
								    2_7_8,
								    2_5_8,
								    2_7_7,
								    2_5_8,
								    0,
								    2_5_8,
								    2_8_8,
								    2_5_8,
								    3_1_9,
								    2_5_8,
								    0,
								    2_5_8,
								    0,
								    2_5_8,
								    0,
								    2_5_8,
								    0,
								    2_5_8,
								    2_8_7,
								    2_5_8,
								    3_1_5,
								    2_5_8,
								    2_8_9,
								    2_5_8,
								    2_7_8,
								    9_9,
								    2_6_9,
								    2_6_6,
								    2_6_2,
								    8,
								    2_5_9,
								    2_4_1,
								    4,
								    2_1_7,
								    2_3_0,
								    2_6_8,
								    2_6_6,
								    5_5,
								    1_6_8,
								    1_0_6,
								    7_5,
								    1_9_3,
								    2_6_6,
								    2_2_3,
								    2_7,
								    4_9,
								    2_6,
								    2_8_2,
								    2_5,
								    2_6_4,
								    2_9_9,
								    1_9,
								    2_6,
								    0,
								    2_5_8,
								    2_7_7,
								    1_1_7,
								    8_6,
								    9_3,
								    1_7_6,
								    1_8_3,
								    2_7_0,
								    1_1,
								    2_6_2,
								    4_2,
								    6_1,
								    2_6_5,
								]

								self.assertListEqual(a	,  self.big_tokenizer.encode(a		)		)







	@require_torch
	@slow
	def   _lowerCamelCase							(   self						:List[Any]		)						->					List[Any]:
								import torch

								from transformers import ReformerConfig, ReformerModel

								# Build sequence
								__UpperCamelCase      :  Tuple       =   list(self.big_tokenizer.get_vocab().keys()		)[:1_0]
								__UpperCamelCase      :  Union[str, Any]       =   " ".join(a		)
								__UpperCamelCase      :  Tuple       =   self.big_tokenizer.encode_plus(a	,  return_tensors="pt"		)
								__UpperCamelCase      :  Optional[int]       =   self.big_tokenizer.batch_encode_plus([sequence, sequence]	,  return_tensors="pt"		)

								__UpperCamelCase      :  Optional[Any]       =   ReformerConfig()
								# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
								__UpperCamelCase      :  Tuple       =   encoded_sequence["input_ids"].shape
								__UpperCamelCase      :  int       =   ReformerModel(a		)

								# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
								assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

								with torch.no_grad():
															model(**a		)
															model(**a		)







	@slow
	def   _lowerCamelCase							(   self						:Optional[Any]		)						->					Any:
								# fmt: off
								__UpperCamelCase      :  Any       =   {"input_ids": [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
								# fmt: on

								# This tokenizer does not know some characters like ")".
								# That is the reason why we use very simple texts here.
								# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
								__UpperCamelCase      :  List[str]       =   [
								    "This is a very simple sentence.",
								    "The quick brown fox jumps over the lazy dog.",
								]

								self.tokenizer_integration_test_util(
								    expected_encoding=a	,  model_name="google/reformer-crime-and-punishment"	,  revision="0e6c3decb8211d49bf881013425dc8b0448b3f5a"	,  padding=a	,  sequences=a	,  )